aws·Aug 10, 2026·3 min readCurated summary

AWS Weekly Roundup: AWS Heroes Summit, Web Search on Amazon Bedrock, Dogwood, Kiro Crew, and more (August 10, 2026) | Amazon Web Services

The August 10, 2026 AWS Weekly Roundup highlights the AWS Heroes Summit and several launches focused on AI agents, modernization, data retrieval, and developer productivity. Major updates include web search for Amazon Bedrock, vector search in DynamoDB, dedicated AgentCore runtime instances, and faster Lambda networking. AWS also introduced Dogwood for temporal agent governance, portable Agent Plugins, and Kiro Crew for persistent multi-agent development. ## AWS Heroes Summit - AWS Heroes from around the world gathered for technical collaboration, deep dives, and feedback sessions with AWS teams. - The invite-only event featured: - A fireside chat with AWS CEO Matt Garman. - An AMA with James Hamilton. - Breakout sessions led by AWS product teams. - The summit emphasized knowledge sharing, community support, and collaboration between AWS experts and internal teams. ## New Amazon Bedrock and AI Capabilities - **Web Search on Amazon Bedrock** - OpenAI models, including GPT-5.4, GPT-5.5, and GPT-5.6 variants, can browse and retrieve current information from the internet. - Applications can use real-time web content while keeping data within a secured AWS environment with zero data egress. - **Runtime Instances for Bedrock AgentCore** - AI agents can run on dedicated runtime instances. - This provides more predictable performance, execution control, and costs. - **Vector Search for DynamoDB** - Applications can store and query vector embeddings alongside existing DynamoDB data. - This supports semantic retrieval and grounding for AI agents without deploying a separate vector database. ## Modernization and Lambda Networking - **AWS Transform continuous modernization** - Now generally available for analyzing and remediating technical debt across repositories. - Supports ongoing modernization of mainframe and legacy workloads rather than one-time migration projects. - Integrates with the AWS Transform Kiro Power and agent plugins. - **Higher Lambda bandwidth** - Lambda functions configured with at least 2 GB of memory and running outside a VPC can receive proportional network bandwidth increases. - Bandwidth ranges from 625 Mbps at 2 GB to 3,000 Mbps at 10 GB. - The change benefits data-intensive workloads and communication with other AWS services. ## Agent Governance and Portability - **Dogwood** - AWS open-sourced Dogwood, a governance language designed for AI agents. - It extends Cedar policies with temporal conditions. - AgentCore temporal policies can make decisions based on an agent’s prior actions within a session, not only the current request. - **Agent Plugins** - AWS supports an open, vendor-neutral specification for packaging agent extensions. - Developers can package an extension once and use it across compatible tools such as Kiro, VS Code, and Cursor. ## Kiro Crew - Kiro Crew provides a persistent workspace for collaborative, multi-agent development. - It is designed for work spanning multiple repositories, tools, and days rather than a single chat session. - Developers can run several efforts in parallel or delegate tasks to subagents that report results later. AWS’s latest updates point toward more capable, governed, and portable AI-agent workflows, while also improving modernization tools and infrastructure performance. Developers can explore the related documentation, AWS events, and the AWS Builder Center for further resources.

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AI AgentsAWSAmazon BedrockAws Lambda+4

aws·Jul 20, 2026·3 min readCurated summary

AWS Weekly Roundup: One-click Lambda setup prompt, OpenAI GPT-5.6 models on Bedrock, and more (July 20, 2026) | Amazon Web Services

The AWS Weekly Roundup highlights new tools for AI-assisted serverless development, including a one-click Lambda setup prompt that configures coding agents with AWS Serverless skills and MCP. It also covers major service updates such as OpenAI models on Bedrock, faster S3 storage-class transitions, self-managed Lambda code storage, and Cognito password-hash imports. Additional stories include SQS’s 20th anniversary, open agent protocols, DynamoDB bulk operations, and a resolved Cost Explorer billing-data incident. ## One-Click Lambda Setup for Coding Agents - The Lambda console now provides a prompt that configures AI coding agents with: - AWS Serverless skills - The Serverless Model Context Protocol (MCP) server - Embedded serverless best practices - The setup guide supports Claude Code, Kiro, Cursor, GitHub Copilot, Codex, Devin Desktop, and OpenCode. - Developers can copy the setup URL into their preferred agent: ```text fetch https://docs.aws.amazon.com/lambda/latest/dg/samples/aws-lambda-agent-setup.md ``` - AWS’s Agent Toolkit can also install the AWS MCP Server, providing current AWS knowledge and controlled resource access. ## Major AWS Service Launches - **OpenAI GPT-5.6 models on Amazon Bedrock** - Sol: flagship reasoning - Terra: balanced performance - Luna: faster, lower-cost inference - All are available through Bedrock’s Responses API and its high-performance inference engine. - **Same-day S3 transitions** - Objects can transition to S3 Standard-IA or S3 One Zone-IA on the day they are created. - The previous 30-day minimum retention period in S3 Standard no longer applies. - These classes can reduce storage costs by up to 40% while retaining millisecond access. - Suitable for backups, log analytics, and compliance data that becomes cold quickly. - **Self-managed Lambda code storage** - Lambda can reference code directly from customer-owned S3 buckets. - Lambda no longer needs to create intermediate copies. - This removes code-storage limits and can shorten activation times after deployments. - **Cognito password-hash imports** - CSV user imports can now include password hashes. - Users can sign in immediately with existing credentials instead of resetting passwords. - Import configuration specifies the source system’s hashing algorithm. ## Additional AWS Updates - **Amazon SQS at 20** - SQS continues to provide scalable decoupling between message producers and consumers, two decades after its public launch. - **Open protocols with Strands Agents SDK** - An example demonstrates how MCP, A2A, UTCP, AG-UI, and x402 can work together when building AI agents. - **Open-source DynamoDB Bulk Executor** - Performs large-scale table operations without custom code. - Supports `count`, `find`, `delete`, and `update` commands. - **Kiro CLI for AWS Support** - MCP integration combines investigation, documentation lookup, and support-case creation. - Examples cover Glue failures, Lambda cold starts, and WAF false positives. ## Cost Explorer Incident - Some customers saw inaccurate estimated billing and usage data in Cost Explorer. - The issue generated erroneous budget and cost-anomaly alerts. - AWS resolved the incident and is conducting a retrospective to improve billing-incident prevention and response. AWS’s latest releases emphasize faster serverless development, more capable AI tooling, lower-cost storage, and easier automation of operational tasks. Developers should explore the Lambda agent setup and Agent Toolkit while reviewing the new storage, identity, and bulk-operation capabilities for relevant workloads.

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AI AgentsAmazon BedrockAmazon S3Aws Lambda+4

aws·Jun 22, 2026·4 min readCurated summary

AWS Weekly Roundup: NY Summit recap, Local Zone in Hanoi, Grok 4.3 in Bedrock, price reductions, and more (June 22, 2026) | Amazon Web Services

AWS’s June 22, 2026 roundup centers on the New York Summit’s focus on AI agents that continuously create value across work, security, software development, and customer applications. It also highlights new regional infrastructure, developer tools, Bedrock models, data capabilities, performance improvements, and several price reductions. Overall, AWS is emphasizing agent-driven automation while lowering barriers and costs for building and operating cloud workloads. ## New York Summit: Agents Across the AWS Stack - **Agents for working:** Amazon Quick supports autonomous, multi-step agents and provides a prioritized activity feed combining email, Slack, calendars, and tasks. - **Agents for securing:** AWS Continuum is an AI-native security service designed to reason about, validate, and remediate vulnerabilities across the development lifecycle. - AWS Security Agent adds threat modeling, pull-request scanning and remediation, and IDE integrations through Kiro, Claude Code, and MCP. - **Agents for building:** Kiro, AWS DevOps Agent, and AWS Transform support continuous coding, deployment, release assessment, and autonomous modernization. - Kiro now includes a native iOS app. - AWS DevOps Agent can evaluate code changes before production release. - **Agents customers create:** Amazon Bedrock AgentCore adds a generally available infrastructure and orchestration harness, Web Search, Managed Knowledge Base, Guardrails integrations, and AWS Context for mapping organizational data relationships. ## New Infrastructure and Developer Services - **AWS Local Zone in Hanoi:** The new `ap-southeast-1-han-1a` zone supports Amazon S3 and Amazon EBS Local Snapshots, helping customers satisfy local data residency and backup requirements. - **AWS Blocks:** This preview open-source TypeScript framework provides a local environment with Postgres, authentication, and real-time messaging without requiring an AWS account. Applications can later deploy to AWS without code changes, with optional CDK integration. - **AWS Management Console Private Access:** Enterprises can access the AWS Console from isolated VPCs without internet connectivity, supporting air-gapped security models. - **AWS Marketplace Storefront:** Partners can publish branded catalogs of AWS Marketplace solutions on their own websites or applications. ## AI, Data, and Agent Capabilities - **Grok 4.3 in Amazon Bedrock:** xAI’s model is available for reasoning, agentic, and enterprise workflows, with tool calling, structured output, and response streaming. - **Amazon S3 annotations:** Objects can now carry up to 1 GB of mutable, queryable context, reducing the need for separate metadata systems in AI-agent and autonomous workflows. - **Strands Agents:** The open-source toolkit adds improved Harness SDK context management, isolated execution through Strands Shell, and chaos testing and red-team capabilities in Strands Evals. - **NVIDIA-powered EC2 G7:** G7 instances use NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs and sixth-generation Intel Xeon processors, delivering up to 4.6 times the AI inference performance and 2.1 times the graphics performance of G6 instances. ## Performance and Security Improvements - **Faster Amazon ECS auto scaling:** Support for 20-second metrics reduces scale-out trigger time from 363 to 86 seconds and total scaling and task provisioning time from 386 to 109 seconds in AWS benchmarks. - **Palo Alto Networks DNS Security:** Route 53 Resolver DNS Firewall can apply PANW Advanced DNS Security protections directly, without separate firewalls or VPC changes. ## Price Reductions - **Amazon S3 Vectors:** Query charges for large vector indexes fall by up to 80%, with no application changes required. - **Amazon GameLift Servers:** Generation 6 and newer instances now include free inbound and outbound network bandwidth for both On-Demand and Spot usage. - **AWS Marketplace professional services:** Listing fees drop from 2.5% to 0.5%, reducing transaction costs for consulting, managed services, and software partners. AWS’s latest direction is to combine increasingly autonomous agents with faster infrastructure, broader model choice, stronger security, and lower operating costs. Developers and organizations should evaluate Bedrock AgentCore, AWS Blocks, S3 annotations, and the new regional and private-access options where they can simplify agent development or satisfy data and security requirements.

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AI AgentsGenerative AIPostgreSQLTypeScript+4

aws·Jun 17, 2026·3 min readCurated summary

Top announcements of the AWS Summit in New York, 2026 | Amazon Web Services

AWS Summit New York 2026 focused on making AI agents more capable, secure, autonomous, and easier to operate in production. Announcements spanned Amazon Bedrock AgentCore, security automation, developer tooling, workplace agents, and S3 data management. The overall direction is toward agents that can access governed knowledge, act independently, and continuously improve while remaining subject to enterprise controls. ## Building More Capable Agents - **Amazon Bedrock Managed Knowledge Base** simplifies enterprise RAG with native data connectors, Smart Parsing for multi-format data, and an Agentic Retriever for complex, multi-step queries. - **Web Search for Bedrock AgentCore** provides managed, current, cited web knowledge while keeping data within the customer’s secured AWS environment. - **Bedrock AgentCore harness** is now generally available, allowing developers to define an agent’s model, tools, skills, and instructions through configuration rather than custom orchestration loops. - **AWS Context**, coming soon, will map relationships across organizational data into a knowledge graph. Agents will be able to use governed relationships, business rules, and domain knowledge at runtime. - **AWS WAF Bot Control** lets publishers and content owners price, meter, and collect payment from AI bots accessing content and APIs, with scoped access enforced at the edge. ## Securing Agents and Applications - **AWS Continuum**, available in gated preview, aggregates vulnerability findings, ranks them by business impact, verifies exploitability, and routes fixes through existing development processes. - **AWS Security Agent**, now part of Continuum, adds: - Threat modeling based on the STRIDE framework - Pull-request code scanning and remediation across major Git platforms - IDE integrations through Kiro, Claude Code, and MCP - These tools are intended to let developers perform security reviews and address vulnerabilities without leaving their normal workflows. ## AI-Assisted Software Development - **Kiro for iOS**, in gated preview, lets developers start, monitor, steer, and approve Kiro sessions from a phone, including reviewing diffs and approving changes without keeping a laptop running. - **AWS DevOps Agent** adds release readiness reviews and autonomous release testing. It evaluates changes against natural-language standards and tests them in production-like environments. - **AWS Transform continuous modernization**, in preview, scans repositories against configurable technical-debt baselines and can autonomously generate remediation pull requests. ## Autonomous Workplace Agents - New **Amazon Quick autonomous agents** can work in the background with defined expertise, tone, permissions, and tools. - Example use cases include: - A finance agent processing incoming orders - A sales agent analyzing CRM, email, and Slack activity - Agents drafting follow-ups, identifying risks, and recommending next steps - A new **activity feed** combines email, messaging, calendars, and tasks into a prioritized view that adapts to the user’s communication and work patterns. ## Richer Metadata for Amazon S3 - **S3 annotations** allow up to 1 GB of mutable, queryable context to be attached directly to an object. - The feature targets AI agents and autonomous workflows that need to discover and interpret data without maintaining separate metadata systems. AWS’s announcements point toward an agent ecosystem that combines managed knowledge retrieval, web access, security automation, autonomous development workflows, and persistent workplace assistance. Organizations adopting these capabilities should pair autonomy with strong governance, scoped permissions, and continuous validation in production.

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AI AgentsAmazon BedrockRAGAmazon S3+4

aws·Jun 17, 2026·3 min readCurated summary

Introducing Amazon Bedrock Managed Knowledge Base for faster, more accurate enterprise AI applications | Amazon Web Services

Amazon Bedrock Managed Knowledge Base is a managed service for building enterprise generative AI applications over proprietary data. It abstracts storage, retrieval, embeddings, reranking, and model selection while adding native connectors, automated parsing, and agentic retrieval. The result is a faster way to create scalable, accurate RAG-based agents without maintaining the underlying infrastructure. ## Enterprise Knowledge Base Challenges - Enterprise data is distributed across systems with different formats, permissions, and access controls. - RAG accuracy requires ongoing experimentation with parsing, chunking, embedding models, and retrieval behavior. - Organizations must support either massive knowledge bases containing millions of documents or thousands of smaller ones while controlling cost and enforcing security. - These infrastructure tasks divert developers from building application functionality. ## Managed RAG Infrastructure - Managed Knowledge Base combines storage, retrieval, embeddings, reranking, and foundation model selection into one managed primitive. - The service automatically selects and manages default embedding, reranking, and foundation models. - It can scale end-to-end RAG pipelines with only a few lines of code. - Through Amazon Bedrock AgentCore Gateway, it is available as a pre-built target with automatically generated role-based permissions, observability, and evaluation metrics. ## Native Data Connectors - Six built-in connectors ingest enterprise content and permissions directly from: - Amazon S3 - SharePoint - Confluence - Web Crawler - Google Drive - OneDrive - Connectors eliminate the need to build and maintain application-specific ingestion logic. - IAM roles are created automatically, with the option to customize permissions. ## Smart Parsing Smart Parsing automatically chooses ingestion and parsing techniques based on the source and content type. - Connector-specific models preserve important structure: - Web Crawler retains HTML structure, embedded images, and tables. - SharePoint preserves document hierarchies and relationships. - Multimodal processing detects document content types, identifies bounding boxes, and uses foundation models for extraction and captions. - Optimized chunking uses document structure and content type to balance retrieval quality and performance. - Developers can rely on defaults or customize chunking strategies for advanced use cases. ## Agentic Retriever Agentic Retriever is designed for complex questions requiring multi-step reasoning and retrieval. - It decomposes a query into a sequence of subquestions. - It performs multihop retrieval within one knowledge base or across multiple knowledge bases. - It evaluates intermediate results and stops once sufficient relevant passages have been found. - For example, it can connect a team’s cloud budget with an expense policy governing annual prepayments—something a single retrieval step might miss. - Retrieved context can then support more accurate, grounded responses from enterprise agents. ## Getting Started - Create a Managed Knowledge Base from the Amazon Bedrock AgentCore or Amazon Bedrock console. - Choose **Create Managed KB** and select **Unstructured Vector Store KB**. - Select a supported data connector and accept the optimized defaults. - After synchronization, connect the knowledge base to an agent or expose it as a tool for a foundation model. Managed Knowledge Base is best suited to teams that want production-ready enterprise RAG without assembling and operating every component themselves, while retaining customization options for specialized accuracy or governance requirements.

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AI AgentsGenerative AIAmazon BedrockRAG+2

aws·Jun 17, 2026·3 min readCurated summary

Announcing Web Search on Amazon Bedrock AgentCore: Ground your AI agents in current, accurate web knowledge | Amazon Web Services

Amazon Bedrock AgentCore Web Search is now generally available, giving AI agents access to current, cited web information without sending prompts or queries outside a customer’s AWS environment. Built into AgentCore Gateway through the Model Context Protocol (MCP), it combines Amazon’s web index with structured knowledge graph data to provide relevant snippets, URLs, titles, dates, and verified facts. The service lets developers ground agent responses in up-to-date information without building or managing search infrastructure themselves. ## How Web Search Works - Agents submit natural-language queries through an AgentCore Gateway. - The tool returns: - Relevant web snippets - Source URLs - Page titles - Publication dates - Results are powered by: - Amazon’s web search index - Amazon Knowledge Graph data - The combination of web results and structured facts is intended to improve relevance and accuracy over conventional search alone. - Retrieved information can support both grounded responses and follow-up agent actions. ## Secure, Managed Agent Integration - Web Search is a fully managed connector target for Bedrock AgentCore Gateway. - Customer prompts and retrieval queries remain within the AWS environment rather than being sent to external search API providers. - Developers avoid implementing search integrations and operating related infrastructure. - The service supports enterprise governance requirements and secured data workflows. ## Getting Started - Create or open a Bedrock AgentCore Gateway in the AWS console. - Select: - **MCP** as the target protocol - **Connectors** as the target type - **Web Search** as the preconfigured target - After the Gateway URL is created, interact with the tool using: - API calls - AWS CLI - MCP Python SDK - Strands MCP Client - MCP Inspector - MCP Inspector can be used to connect to the Gateway, enter a query, and run the Web Search tool for testing and debugging. - Web Search can also be added to an existing Gateway. ## Early Customer Use Cases - **Benchling** uses Web Search to combine institutional scientific data with published literature, helping researchers generate more complete answers and hypotheses in a governed environment. - **Gen Digital** uses the tool in Norton Revamp to create current online reputation content based on real-world developments. - Customers highlighted the value of current information, AWS-native security, and keeping search queries within their trusted environment. ## Availability and Pricing - Web Search is generally available in **US East (N. Virginia)**. - It is offered at no additional cost initially. - Customers pay applicable Gateway data transfer charges. - New AWS customers may receive up to $200 in Free Tier credits. Developers can try Web Search through the Amazon Bedrock AgentCore console and consult the Gateway documentation for implementation details.

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AI AgentsModel Context ProtocolAmazon Bedrock AgentcoreKnowledge Graph+2

aws·Jun 16, 2026·3 min readCurated summary

Amazon S3 annotations: attach rich, queryable context directly to your objects | Amazon Web Services

Amazon S3 annotations let organizations attach rich, mutable context directly to objects without rewriting the underlying data. Each object can have up to 1,000 named annotations, with a combined limit of 1 GB, in formats such as JSON, XML, YAML, or plain text. The annotations move with objects during copying and replication, can be queried through S3 Metadata tables, and are intended to support large-scale analytics and AI-driven workflows. ## Richer Metadata at Object Scale - Annotations address limitations of existing S3 metadata features: - System metadata describes properties such as size and storage class. - Object tags support operational tasks but are limited to 10 immutable tags. - User-defined metadata is limited to approximately 2 KB of headers and must be supplied during upload. - Annotations can be modified or deleted independently without rewriting the object. - Each annotation has a unique name and can hold up to 1 MB, with up to 1 GB total per object. - Supported content includes structured formats such as JSON, XML, and YAML, as well as plain text. ## Supporting AI and Industry Workflows - AI-generated transcripts, summaries, classifications, ratings, and technical specifications can remain attached to the relevant data. - Media companies can store transcripts, moderation results, subtitles, licensing information, and media specifications alongside video assets. - Financial institutions can attach investment summaries and sentiment analysis to research documents for autonomous discovery. - Life sciences organizations can add regulatory status, cohort information, and approval chains to clinical-trial data. - Annotations remain available for objects in archival storage classes without restoring the objects or paying retrieval charges. ## Object Lifecycle and Synchronization - Annotations automatically move with objects during copy, replication, and cross-Region transfers. - S3 removes annotations when the associated object is deleted. - This reduces dependence on separate metadata databases or sidecar files and avoids synchronization overhead. - Different teams or enrichment processes can maintain separate annotations on the same object without overwriting one another. ## Working with Annotations - IAM or bucket policies must grant: - `s3:PutObjectAnnotation` - `s3:GetObjectAnnotation` - The main APIs are: - `PutObjectAnnotation` to create or update an annotation - `GetObjectAnnotation` to retrieve one annotation - `ListObjectAnnotations` to view all annotations on an object - `DeleteObjectAnnotation` to remove an annotation - Reusing an annotation name with `PutObjectAnnotation` updates its content. - For multipart uploads, annotations are added after the upload is completed. - The example uses separate `mediainfo` JSON and `ai_summary` text annotations on a video object. ## Querying Through S3 Metadata - When S3 Metadata is enabled, annotations automatically populate managed annotation tables. - These tables can be queried with Amazon Athena and other analytics engines. - The S3 Tables MCP server provides a standardized interface for AI models and agents to query annotation data using natural-language workflows. - This enables discovery across large collections of S3 objects without retrieving the objects themselves. S3 annotations are most useful when object context is large, frequently updated, and needed for cross-object search or AI automation. Organizations should use them to keep business and machine-generated metadata directly associated with the data while using S3 Metadata tables for scalable querying.

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AI AgentsAmazon S3JsonAws Cli+3

aws·Jun 15, 2026·4 min readCurated summary

AWS Weekly Roundup: AWS FinOps Agent in preview, Gemma 4 on Bedrock, Kiro Pro Max, and more (June 15, 2026) | Amazon Web Services

AWS’s June 15, 2026 roundup highlights rapid adoption of AI-native development, a preview of the AWS FinOps Agent, and major infrastructure and model releases. AWS reports significant productivity gains from agent-assisted engineering, while new services target cost optimization, observability, security, and developer workflows. The overall direction is toward more autonomous, context-aware tools integrated throughout the AWS ecosystem. ## AI-Native Development at AWS - Amazon engineering teams are restructuring development around AI agents. - A six-engineer team rebuilt the Amazon Bedrock inference engine in 76 days, compared with an original estimate of 30 developers over 12–18 months. - Structured Amazon Stores pilots achieved a median 4.5× improvement in normalized deployment velocity, with some teams exceeding 10×. - Recommended practices for “frontier teams” include: - Building steering files, coding standards, and structured repositories to provide agent context. - Expecting an initial productivity slowdown while workflows change. - Maintaining a backlog of well-scoped tasks that agents can execute in parallel. - Writing structured specifications before generating code. - Moving testing earlier so agents can detect and correct issues. - AWS cautions that commit velocity is only one measure; future work will address release management, operations, security, and end-of-life upgrades. ## AWS FinOps Agent Preview - AWS FinOps Agent helps finance and engineering teams: - Answer AWS cost questions and generate reports. - Identify rightsizing, idle-resource, and Savings Plans opportunities. - Investigate cost anomalies automatically. - Schedule recurring FinOps workflows. - Recommendations come from AWS Cost Optimization Hub and AWS Compute Optimizer. - The agent can open Jira tickets and post anomaly-investigation findings to Slack. ## New EC2 M9g and M9gd Instances - M9g and M9gd instances use AWS Graviton5 processors and the sixth-generation Nitro System. - They provide: - Up to 25% better compute performance than Graviton4. - Up to 35% faster web applications and ML inference. - Up to 30% faster database performance. - Up to 15% more network bandwidth and 20% more EBS bandwidth than M8g. - Graviton5 adds PCIe Gen6, DDR5-8800 memory, and a 5× larger L3 cache. - The Nitro Isolation Engine uses formal verification to provide mathematically proven VM isolation. - M9gd adds up to 11.4 TB of local NVMe SSD storage and 30% higher IOPS than M8gd. - Instance Bandwidth Configuration can shift bandwidth between EBS and VPC networking by up to 25%. ## Bedrock Model Updates - Anthropic Claude Fable 5 launched on Bedrock with: - Extended asynchronous task execution. - Advanced vision for diagrams, charts, and PDFs. - Proactive self-verification. - Access required Data Retention API opt-in and 30-day retention for inputs and outputs. - Anthropic later asked AWS to revoke access to Claude Fable 5 and Claude Mythos 5 in response to a U.S. government export-control directive. - Google DeepMind’s Gemma 4 models are available in three versions: - Gemma 4 31B for reasoning and coding, with a 256K-token context window. - Gemma 4 26B-A4B, a mixture-of-experts model for cost- and latency-sensitive use. - Gemma 4 E2B for low-latency interactive applications. - All variants support function calling, structured output, reasoning, streaming, multimodal inputs, and more than 35 languages. ## OpenSearch MCP Apps for Agentic Observability - OpenSearch MCP Apps bring logs, traces, metrics, and alerts into compatible agentic environments such as Claude Desktop and VS Code. - Agents can investigate incidents across OpenSearch and Amazon Managed Service for Prometheus. - Each tool call produces both: - A text summary for the agent. - An interactive visualization displayed in the conversation. - Available tools cover log, metric, and trace analysis; service performance; topology; agent health; cluster health; dynamic visualizations; and instrumentation scoring. ## Additional AWS Updates - AWS CLI v1 is entering maintenance mode: - Dependencies such as `botocore` and `s3transfer` will be vendored into the CLI. - Future releases will focus on critical bugs and security fixes. - AWS recommends migrating to CLI v2. - AWS Workload Credentials Provider provides short-term credentials for applications running outside AWS, including on-premises and third-party environments, without requiring long-term access keys. - Kiro Pro Max adds higher usage limits, access to newer frontier models, and additional agentic development capabilities for professional teams. AWS’s latest releases suggest that teams should prepare for AI agents to handle not only code generation, but also cost management, observability, testing, and operational workflows. Migrating to AWS CLI v2, evaluating Bedrock’s newest models, and establishing strong repository context and specifications are practical next steps.

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AI AgentsModel Context ProtocolAmazon BedrockAmazon Ec2+4

aws·Jun 10, 2026·3 min readCurated summary

Now available: Amazon EC2 M9g and M9gd instances powered by new AWS Graviton5 processors | Amazon Web Services

AWS has generally released Amazon EC2 M9g and M9gd instances, powered by its Graviton5 processors. They deliver higher compute, memory, networking, and storage performance while improving energy efficiency and security. M9g targets general-purpose workloads, while M9gd adds up to 11.4 TB of local NVMe SSD storage for low-latency applications. ## Graviton5’s Performance Improvements - Graviton5 provides up to: - 25% better compute performance than Graviton4 - 35% faster web applications and ML inference - 30% faster database workloads - It includes: - 192 CPU cores - Five times more L3 cache - Up to 33% lower inter-core latency - DDR5-8800 memory - PCIe Gen6 support - These features improve concurrency, memory bandwidth, and CPU-heavy workloads such as agentic AI, code generation, real-time reasoning, and multi-step orchestration. - Customer results include: - ClickHouse: 36% better performance than M8g without code changes - Honeycomb: 36% higher throughput per core - HubSpot: up to 60% shorter MySQL query duration ## Networking and Storage Enhancements - M9g and M9gd provide: - Up to 15% higher network bandwidth - Up to 20% higher EBS bandwidth on average - Up to twice the network bandwidth on the largest instance - Instance Bandwidth Configuration lets customers shift bandwidth between EBS and VPC networking by up to 25%. - This is useful for databases, query processing, logging, and other workloads with demanding I/O requirements. ## Nitro Isolation Engine - M9g and M9gd introduce the Nitro Isolation Engine, an enhancement to the AWS Nitro System. - It isolates virtual machines by controlling access to: - Memory - CPU register state - I/O devices - The component uses formal verification to mathematically prove isolation behavior rather than relying solely on testing. - AWS describes Nitro as the first formally verified cloud hypervisor. ## M9g General-Purpose Instances - M9g instances provide one vCPU per 4 GiB of memory. - Suitable workloads include: - Application servers and microservices - Web applications and containerized applications - Midsize data stores and caching fleets - Gaming servers - Large-scale Java applications - Code repositories - Agentic AI workloads ## M9gd Instances with Local NVMe Storage - M9gd adds up to 11.4 TB of local NVMe SSD storage. - It offers up to 30% higher IOPS and storage performance than Graviton4-based M8gd instances. - Recommended for: - Low-latency caches and scratch files - Data and log processing - Media processing - Batch workloads - Key-value stores - Gaming and application servers ## Availability and Migration Resources - M9g and M9gd are available in: - US East (N. Virginia) - US East (Ohio) - US West (Oregon) - Europe (Frankfurt) - Purchasing options include Savings Plans, On-Demand, Spot Instances, Dedicated Instances, and Dedicated Hosts. - AWS provides a Graviton Getting Started Guide, Graviton Savings Dashboard, and AWS Transform for migrating Java applications from x86 to Graviton. Organizations seeking better compute efficiency, higher I/O throughput, or lower-cost Arm infrastructure should evaluate M9g for general-purpose workloads and M9gd when local NVMe storage is required.

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AI AgentsAmazon Ec2Aws Graviton5Aws Nitro System+4

aws·Jun 5, 2026·2 min readCurated summary

Try the new console experience in Amazon Bedrock, optimized for Anthropic- and OpenAI-compatible APIs | Amazon Web Services

Amazon Bedrock introduces a refreshed console centered on the `bedrock-mantle` inference engine, which supports Anthropic Messages, OpenAI Responses, and OpenAI Chat Completions APIs. The experience is designed to streamline model discovery, evaluation, application development, and production setup for GPT, Claude, and open-weight models. It complements the existing console, which remains available for features such as Agents, Knowledge Bases, Guardrails, fine-tuning, and `bedrock-runtime` APIs. ## Model Catalog and Comparison - Browse supported models in a unified catalog. - Compare up to three models by: - Capabilities and modalities - Context window and token limits - Pricing and input/output costs - Service quotas - Regional availability - Use side-by-side evaluations with identical prompts to compare model responses. ## Project-Based Workflow - Create projects that organize model assignments, evaluations, API keys, and application setup. - The project dashboard displays: - Inference requests and errors over selected date ranges - Recently used models - Total token usage - Tokens per minute - Requests per minute - Tokens per inference request - These metrics can guide model selection, prompt optimization, and workload consistency. ## Application Setup and Live Documentation - The console provides project-specific setup instructions for Anthropic and OpenAI SDKs. - Developers can select an SDK, programming language, and authentication method. - It generates terminal commands, `.env` configuration, and sample requests for quick testing. - Live API documentation automatically inserts the project’s model ID, Region, `bedrock-mantle` endpoint, and API key reference. - Documentation updates automatically when project models or settings change. ## AI Coding Agent Integration - Projects can connect coding agents such as Claude Code, Cline, Codex, Cursor, and OpenCode. - Setup instructions cover: - Installing the selected agent - Using AWS IAM credentials or Bedrock API keys - Configuring environment variables - Routing agent requests through Bedrock ## Availability The new console is available in Regions offering `bedrock-mantle`, including locations in the United States, Asia Pacific, Europe, and South America. Users can access it through the existing Bedrock console by selecting **Try the Bedrock Mantle Console**, while the traditional console remains available for fully managed Bedrock capabilities. Developers building with Anthropic- or OpenAI-compatible APIs can use the new console to move from model evaluation to application testing and deployment with less manual configuration.

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AI AgentsGenerative AIAmazon BedrockAws Sdk+3

aws·Jun 1, 2026·4 min readCurated summary

AWS Weekly Roundup: Claude Opus 4.8 on AWS, Aurora MySQL with Kiro Powers, and more (June 1, 2026) | Amazon Web Services

AWS’s latest roundup highlights a rapid shift toward AI-augmented software development, illustrated by workshops where 17 teams delivered nearly 20 use cases in two days. The centerpiece is Anthropic Claude Opus 4.8, now available through Amazon Bedrock and the Claude Platform on AWS, alongside tools that improve resilience, agentic search, migration analysis, database management, and desktop streaming. Together, these launches show AWS moving toward real-time, AI-assisted development and operations. ## AI-Driven Development and Claude Opus 4.8 - AI-DLC workshops are helping teams deliver software use cases at unprecedented speed, especially when combined with Claude Code on Amazon Bedrock. - Development roles are increasingly consolidating into smaller, AI-augmented teams. - AWS account teams are shifting from producing advisory documents to building solutions collaboratively with customers. - Claude Opus 4.8 is Anthropic’s most capable generally available model and is designed for: - Agentic coding and extended autonomous tasks - Long-document analysis and knowledge work - Deeper reasoning and error recovery - Maintaining context across lengthy coding sessions - On Amazon Bedrock, it includes AWS features such as Guardrails, Knowledge Bases, and data-residency controls. - The Claude Platform on AWS provides Anthropic’s native APIs with unified AWS billing. ## AWS Resilience Hub - The next-generation Resilience Hub provides a unified way to define resilience standards, assess applications, and demonstrate compliance. - Modular policies cover: - Service-level objectives - Multi-Availability Zone and multi-Region disaster recovery - Data recovery - Generative AI assessments align with AWS Well-Architected and Resilience Analysis Framework guidance. - DNS query-log analysis automatically discovers application dependencies. - AWS Organizations integration enables centralized, organization-wide resilience management. ## OpenSearch Serverless for Agentic AI - Amazon OpenSearch Serverless is now positioned as a managed search and vector engine for agent-based applications. - It scales from zero to thousands of requests per second and is approximately 20 times faster than the previous generation. - AWS claims up to 60% cost savings compared with peak-provisioned clusters. - New capabilities include GPU acceleration and dedicated `SEARCH` and `VECTORSEARCH` collection types. - OpenSearch Agent Skills integrate with Vercel, Kiro, Claude Code, and Cursor. ## AWS Transform Assessment and Modernization Tools - AWS Transform can build migration business cases and estimate total cost of ownership. - It accepts data from RVTools, CMDBs, AWS discovery tools, and third-party discovery platforms. - Users can model what-if scenarios involving regions, utilization, and service mappings for services such as EC2, FSx, S3, SQL Server on EC2, and virtual desktops. - Agentic Readiness Analysis and Modernization Analysis scan repositories in roughly 5–30 minutes. - Findings include severity levels, file-level evidence, and AWS-specific remediation recommendations. ## Aurora MySQL with Kiro Powers - Aurora MySQL now integrates with Kiro Powers, which provide curated MCP servers, steering files, and hooks. - Developers can use natural language for both: - Data-plane tasks such as queries and schema changes - Control-plane tasks such as cluster administration - The integration offers guidance for Serverless scaling, RDS-to-Aurora migrations, and replication. - Kiro generates the relevant API calls, SQL, and configuration for developer review before execution. - Installation is available through the Kiro IDE or website. ## Windows Desktop Streaming with WorkSpaces Applications - Amazon WorkSpaces Applications now supports bring-your-own-license Windows Desktop OS deployments. - Customers can stream complete Windows desktops and applications from dedicated AWS hardware. - BYOL removes operating-system fees, leaving compute and streaming infrastructure charges. - Eligible Microsoft 365 Apps for enterprise are supported. - Users receive a consistent experience across local and streamed desktops. ## Additional AWS News - AWS introduced its May 2026 cohort of AWS Heroes. - A new Vercel and AWS Databases integration allows provisioning Aurora PostgreSQL, DynamoDB, and Aurora DSQL through Vercel or v0. - The H0 hackathon offers $160,000 in prizes for applications built with this stack. - AWS GovCloud customers now receive 24/7 support from US-based, US-citizen engineers automatically, without opting in. AWS’s direction is clear: combine capable AI agents with managed infrastructure, developer tools, and operational automation. Teams evaluating these services should focus on practical agentic use cases while retaining review and governance for generated code, infrastructure changes, and database operations.

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AI AgentsAWSAmazon BedrockClaude Opus+4

aws·May 28, 2026·2 min readCurated summary

Introducing the next generation of Amazon OpenSearch Serverless for building your agentic AI applications | Amazon Web Services

Amazon’s next-generation OpenSearch Serverless is a managed search and vector engine optimized for agentic AI applications. It scales from zero to thousands of requests per second, creates resources in seconds, and can reduce costs by up to 60% compared with clusters provisioned for peak demand. The release is generally available across supported AWS commercial Regions and integrates with tools such as Vercel, Kiro, Claude Code, and Cursor. ## Elastic Scaling and Cost Optimization - Scales capacity up to 20 times faster than the previous generation. - Supports scale-to-zero when idle, minimizing compute costs. - Charges separately for compute through OpenSearch Compute Units (OCUs), storage in GB-month, and GPU acceleration where applicable. - Supports capacity limits for indexing and search, with minimum capacity set to zero and configurable maximums. ## Creating Next-Generation Collections - Collections can be created through the Amazon OpenSearch Service console, AWS CLI, or SDKs. - The console’s **Express create** option automatically applies default settings and matching security policies. - At launch, supported collection types are: - Full-text search (`SEARCH`) - Vector search (`VECTORSEARCH`) - Users who need the existing infrastructure can select the classic OpenSearch Serverless generation. - Collections inherit their generation from a parent collection group. Example CLI workflows create a next-generation collection group with standby replicas and then create a search collection within it. ## Integrations for Agent Development - Vercel users can create or connect OpenSearch Serverless collections directly from the Vercel console. - OpenSearch Agent Skills bring search-specific knowledge, best practices, and multi-step workflows into agents using Claude Code, Cursor, and Kiro. - Kiro’s OpenSearch Launchpad provides guided architecture planning for building search applications. ## Availability - The next generation is generally available in all AWS commercial Regions where OpenSearch Serverless is currently offered. - AWS recommends consulting the OpenSearch Serverless documentation and pricing information for configuration and cost details. The release is intended to let developers deploy production-ready search and vector backends quickly, while avoiding the infrastructure management and peak-capacity costs associated with provisioned OpenSearch clusters.

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AI AgentsClaude CodeVector DbAws Cli+4

aws·May 12, 2026·2 min readCurated summary

Amazon Redshift introduces AWS Graviton-based RG instances with an integrated data lake query engine | Amazon Web Services

Amazon Redshift introduces RG instances powered by AWS Graviton, targeting lower-cost, higher-volume analytics for both human users and AI agents. RG instances can run warehouse workloads up to 2.2× faster than RA3 at 30% lower price per vCPU, while an integrated data lake engine enables faster SQL queries across warehouse tables and S3 data. The architecture also removes the need for Redshift Spectrum and its per-terabyte scanning fees. ## Performance and Cost Improvements - RG instances deliver: - Up to 2.2× faster data warehouse workloads than RA3. - 30% lower pricing per vCPU. - Up to 2.4× faster queries on Apache Iceberg data. - Up to 1.5× faster queries on Apache Parquet data. - The improvements are designed for: - Low-latency BI dashboards. - ETL pipelines and near-real-time analytics. - High-volume queries generated by autonomous AI agents. - AWS recommends using the AWS Pricing Calculator to estimate savings for specific workloads. ## Integrated Data Lake Query Engine - RG instances query warehouse tables and S3 data lakes through one engine. - Data lake queries run directly on Redshift cluster nodes rather than through Redshift Spectrum. - Existing external tables, schemas, Spectrum queries, and SQL syntax remain unchanged. - Customers do not need to recreate external tables or modify application code. - Queries remain inside the customer’s VPC, use existing IAM roles, and avoid Spectrum’s former $5-per-terabyte scanning charge. ## Migration and Setup - RG clusters can be created or migrated through: - The AWS Management Console. - AWS CLI. - AWS API. - The integrated data lake engine is enabled by default. - Migration options include: - **Elastic Resize:** In-place migration with approximately 10–15 minutes of downtime for compatible configurations. - **Snapshot and Restore:** Creates an RG cluster from an RA3 snapshot and is useful when configuration changes are needed. ## Availability and Pricing Options - RG instances are available across numerous AWS Regions in North America, Europe, Asia-Pacific, Canada, and South America. - Redshift Provisioned customers can choose: - On-Demand Instances with hourly billing and no commitment. - Reserved Instances for additional savings. RG instances are intended for organizations combining data warehouse and data lake workloads, especially those needing lower costs and fast response times at high query volumes. Customers should test compatibility and use workload-specific pricing estimates before migrating.

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AI AgentsAmazon S3SqlApache Iceberg+4

aws·May 11, 2026·3 min readCurated summary

AWS Weekly Roundup: Amazon Bedrock AgentCore payments, Agent Toolkit for AWS, and more (May 11, 2026) | Amazon Web Services

The May 11, 2026 AWS roundup highlights growing support for autonomous AI agents, especially through Amazon Bedrock AgentCore’s managed payment capabilities. It also covers new tools for building secure agents on AWS, enhanced WorkSpaces automation, faster EC2 instances, and updates across Valkey, vector search, and agentic SRE operations. ## AgentCore Payments for Autonomous Agents - Amazon Bedrock AgentCore previewed managed payments for AI agents. - Agents can autonomously pay for: - APIs - MCP servers - Web content - Other agents - The service was developed with Coinbase and Stripe to handle billing, credentials, and compliance. - Developers can connect: - Coinbase CDP wallets - Stripe Privy wallets - Session-level spending limits help control agent transactions. - Potential applications include research agents purchasing live market data and coding agents calling paid APIs during execution. ## New Tools for Building AI Agents on AWS - **Agent Toolkit for AWS** provides production-ready tools and guidance at no additional charge. - It aims to reduce coding errors and token usage while adding enterprise security controls. - The toolkit replaces AWS Labs’ earlier MCP servers, plugins, and skills. - **AWS MCP Server** is now generally available as a managed, remote MCP server. - It provides secure, authenticated access to AWS services. - Agents interact through a small, standardized set of tools. - It is included in the Agent Toolkit for AWS. ## AI-Controlled Workspaces and New EC2 Instances - **Amazon WorkSpaces for AI agents** entered preview. - Agents can securely access and operate desktop applications. - Managed WorkSpaces environments provide governance and compliance controls. - The capability targets large-scale automation of everyday workflows. - New **EC2 M8idn/M8idb and R8idn/R8idb instances** use sixth-generation Intel Xeon processors and AWS Nitro cards. - They provide up to 43% better compute performance per vCPU than prior generations. - M8idn and R8idn offer up to 600 Gbps of network bandwidth. - M8idb and R8idb offer up to 300 Gbps of EBS bandwidth. ## Valkey and Vector Search Updates - Valkey, the community-driven Redis alternative, marked its second anniversary. - It has surpassed: - 100 million Docker pulls - 225 contributors - 1,500 pull requests - Valkey’s development pace is described as roughly twice that of Redis over the same period. - Valkey 9.0 is available through Amazon ElastiCache. - Amazon Aurora PostgreSQL-Compatible Edition can query billion-scale Amazon S3 Vectors using standard SQL. - Vector similarity searches can be combined with relational filters, such as price, inventory, or tenant, in a single query. ## Agentic Site Reliability Engineering - AWS DevOps Agent can be configured with “Spaces” that define investigation scopes. - It integrates with Amazon CloudWatch, Splunk, GitHub, and Slack. - Webhooks can trigger automated investigations. - The system can create mitigation plans and produce agent-ready specifications for coding tools such as Kiro. AWS’s latest updates emphasize autonomous agents that can transact, operate software, investigate incidents, and interact with cloud services while remaining governed by enterprise security and spending controls. Teams interested in these capabilities should start with the Agent Toolkit for AWS and explore the related previews and documentation.

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AI AgentsModel Context ProtocolAmazon Ec2Amazon Cloudwatch+4

aws·May 5, 2026·2 min readCurated summary

Modernize your workflows: Amazon WorkSpaces now gives AI agents their own desktop (preview) | Amazon Web Services

Amazon WorkSpaces now lets AI agents operate desktop and legacy applications directly, eliminating the need to build APIs or modernize existing software. Agents use managed virtual desktops with IAM authentication, security controls, and auditability through CloudTrail and CloudWatch. The feature is in public preview and supports agent frameworks through the Model Context Protocol (MCP). ## The Challenge of Legacy Applications - Many enterprises depend on applications without modern APIs: - 75% of organizations reportedly run legacy applications. - 71% of Fortune 500 companies rely on mainframe-based processes with limited programmatic access. - Organizations can either delay AI adoption or undertake costly, risky modernization projects. ## AI Agents in Secure WorkSpaces - AI agents operate desktop applications inside managed WorkSpaces environments. - Agents authenticate with AWS Identity and Access Management (IAM). - Existing security and compliance controls remain in place because agents do not run on local machines. - AWS CloudTrail and Amazon CloudWatch provide audit trails. - WorkSpaces supports MCP, making it compatible with frameworks such as LangChain, CrewAI, and Strands Agents. ## Configuring Agent Access - Administrators create a WorkSpaces Applications stack and enable the **Add AI Agents** option. - Agent capabilities can include: - **Computer input:** Clicking, typing, and scrolling. - **Computer vision:** Capturing screenshots so the agent can interpret the interface. - **Screenshot storage:** Saving session images for auditing and debugging. - Administrators define screen resolution and image format. The example uses 1280×720 resolution and PNG images. - Agents connect through a managed MCP endpoint using IAM credentials. ## Automating Unmodified Desktop Workflows - A Strands Agent SDK and Amazon Bedrock example completes a prescription refill by: - Looking up a patient record. - Searching for medication. - Placing the order. - Confirming the refill. - The pharmacy application requires no API, code changes, migration, or awareness that an agent is controlling it. ## Availability - The feature is in public preview at no additional cost. - It is available in selected AWS Regions across the United States, Canada, Europe, and Asia. - Developers can begin with AWS’s GitHub repository or the Amazon WorkSpaces product page. Organizations can use WorkSpaces as a governed execution environment for AI agents, allowing them to automate legacy desktop workflows while postponing or avoiding extensive application modernization.

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AI AgentsModel Context ProtocolAmazon BedrockAmazon Cloudwatch+4